A2 Preparation Guide (Final)

Exam Duration: 3 hours
Total Marks: 180

I. General Preparation Principles

  • Do all tutorial questions until you can perform them without notes.
  • Revise all class examples.
  • Study the theory supporting each practical question.
  • Use the Survival Kits at the end of chapters.
  • Check the Table of Contents in the eBook and ensure topic recognition.
  • Do not “spot” topics. Everything is examinable unless stated otherwise.
  • Treat old notes cautiously. Some legacy model answers are wrong or outdated.

II. What You Do Not Need to Memorize

Item A2 A3
Statistical tables Provided Provided
Formula sheet (as in A1) Provided Provided
or calculations (eBook p.77) Excluded Excluded
SimTalk syntax Excluded Excluded
TPS object definitions Excluded Excluded
Monte Carlo & Contaminating System (NTS) Excluded Excluded
Kolmogorov–Smirnov (K–S) test Excluded Included
Procedure Kim–Nelson (KN) Properties only Properties only

III. Core Study Areas

1. Theory — Q1 (33 Marks)

  • Keywords: Define, Explain, List, Why?, How did you … ?
  • Covers simulation fundamentals, conceptual reasoning, and model understanding.
  • Expect questions linking theory to model logic (e.g., why throughput behaves a certain way).

2. Input Data Analysis: χ², Inverse Transform — Q2 (36 Marks)

Focus on:

  • Chi-squared test (χ²) for testing distributional fit.
  • Inverse transform sampling for generating random variables (continuous and discrete).

Supporting notes (context only, not necessarily direct questions):

  • ODF — discrete variable based on failure counts.
  • Decision variables and space: p.97
    • = specific decision variable combination. (a single solutions combination of decision variables)
    • = decision space (all possible ). (set of all feasible combinations of decision variables)
  • ξ — stochastic components such as service or failure times.

Excluded: Monte Carlo, K-S test.

3. Models — Q3–Q9 (103 Marks)

Know Model 0 (Drive-through / McD) and all other models.

Concept Focus
Throughput limits All models capped by source (tap) rate.
Buffer Allocation Problem (BAP) Behaviour as buffers → ∞.
Trauma Unit Model (TUM) Identify entities and apply Shannon’s world view.
Events Instantaneous occurrences (arrive, start, finish, leave).
System State Variables Throughput, WIP, utilization over time.
Validation inside models Recognise throughput effects when configuration changes.

4. Validation and Conceptual Questions

Goal: Adequate representation, not “accurate.”
Discuss real tests, not theory lists.
Reference notes: Validation

Model Validation Focus
TUP (Trauma Unit Problem) “Stay here after job” invalidates model logic — must leave to scrub/rest.
(r, Q) Inventory Model Low r and Q can still cause high total cost (frequent ordering).
Buffer sizing Observe upper/lower throughput bounds when changing buffers.

5. Output Analysis — Q10 (8 Marks)

  • Half-width () and required sample size () interpretation.
  • Multi-Objective Optimization (MOO): identify Pareto-optimal trade-offs.
  • p-Table: p.78
    • A pairwise comparison table, not ANOVA.
    • Interpret p-values:
      • p > 0.05 → no significant difference.
      • p ≤ 0.05 → significant difference.
    • Used to identify statistically similar or superior systems.
  • Experiment Manager (EM): outputs and interpretation are examinable.

6. Algorithms and Procedures

Procedure Required Knowledge
Genetic Algorithm (GA) Crossover / mutation / population size ↔ exploration; generations ↔ exploitation.
Procedure Kim–Nelson (KN)p.81 A procedure performing just enough replications to separate systems statistically — contrasts with ANOVA’s fixed replications. Know properties, not steps.

IV. Paper Layout

Q Content Marks Focus
1 Theory 33 Define, Explain, List, Why?, How …
2 Input Data Analysis 36 χ², Inverse Transform
3–9 Models 103 Model 0, TUM, BAP, (r,Q), Events, Validation
10 Output Analysis 8 h, n*, MOO, p-table interpretation

Final Advice

  • Understand why systems behave as they do.
  • Link observations to throughput, variability, and adequacy of representation.
  • Apply reasoning, not recall.